language:
- ru
tags:
- toxic comments classification
licenses:
- cc-by-nc-sa
General concept of the model
This model is trained on the dataset of inappropriate messages of the Russian language. The concept of inappropriateness is described in this article presented at the workshop for Balto-Slavic NLP at the EACL-2021 conference. Please note that this article describes the first version of the dataset, while the model is trained on the extended version of the dataset open-sourced on our GitHub or on kaggle. The properties of the dataset is the same as the one described in the article, the only difference is the size.
The model was trained, validated and tested only on the samples with 100% confidence, which allowed to get the following metrics on test set:
| precision | recall | f1-score | support | |
|---|---|---|---|---|
| 0 | 0.92 | 0.93 | 0.93 | 7839 |
| 1 | 0.80 | 0.76 | 0.78 | 2726 |
| accuracy | 0.89 | 10565 | ||
| macro avg | 0.86 | 0.85 | 0.85 | 10565 |
| weighted avg | 0.89 | 0.89 | 0.89 | 10565 |
Licensing Information
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Citation
If you find this repository helpful, feel free to cite our publication:
@inproceedings{babakov-etal-2021-detecting,
title = "Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company{'}s Reputation",
author = "Babakov, Nikolay and
Logacheva, Varvara and
Kozlova, Olga and
Semenov, Nikita and
Panchenko, Alexander",
booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing",
month = apr,
year = "2021",
address = "Kiyv, Ukraine",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.bsnlp-1.4",
pages = "26--36",
abstract = "Not all topics are equally {``}flammable{''} in terms of toxicity: a calm discussion of turtles or fishing less often fuels inappropriate toxic dialogues than a discussion of politics or sexual minorities. We define a set of sensitive topics that can yield inappropriate and toxic messages and describe the methodology of collecting and labelling a dataset for appropriateness. While toxicity in user-generated data is well-studied, we aim at defining a more fine-grained notion of inappropriateness. The core of inappropriateness is that it can harm the reputation of a speaker. This is different from toxicity in two respects: (i) inappropriateness is topic-related, and (ii) inappropriate message is not toxic but still unacceptable. We collect and release two datasets for Russian: a topic-labelled dataset and an appropriateness-labelled dataset. We also release pre-trained classification models trained on this data.",
}